Types of virtual try-on, compared
There are three distinct technologies sold under the name virtual try-on: AR overlay filters, 3D garment simulation on an avatar, and generative AI on the shopper's own photo. They fail in different ways, and the right choice depends on the product category rather than on which is newest.
The three approaches
| Approach | How it works | Strongest for | Weakest at |
|---|---|---|---|
| AR overlay filter | Draws a graphic over a live camera feed, anchored to face or body landmarks | Glasses, makeup, jewellery, watches — rigid objects on a stable anchor | Fabric. Cloth drape, folds and weight cannot be faked by an overlay |
| 3D simulation on avatar | Each garment is modelled in 3D and simulated on a body mesh | Physically accurate fit and size guidance, made-to-measure | Cost and coverage — per-garment 3D work; the result looks rendered, and it is an avatar, not the shopper |
| Generative AI on the shopper photo | A model composes the product photo onto the shopper's photo as a new image | Apparel at catalogue scale; photoreal output showing the actual shopper | Exact measurement claims — it shows how something looks, not a guaranteed size |
What to ask a vendor, whichever approach they sell
- Does it show my shopper, or an avatar? Purchase confidence comes from seeing yourself. An avatar answers a different question.
- What does onboarding a new product cost? If each garment needs 3D work or a special shoot, catalogue coverage will stay low, and coverage is what determines revenue impact.
- Does identity survive? Ask to see outputs at full resolution. A whitened, smoothed or subtly altered face is the most common failure and the most damaging to trust.
- Can several garments be tried together? Outfit-level try-on moves basket size; single-item try-on moves only item conversion.
- What is reported back? Try-on counts are not a result. Conversion and return rate, split by tried vs. not-tried, are.
Where TryOnApp sits, and where it does not
TryOnApp (Turkish market: üstündedene) is the third kind: generative AI on the shopper's own photo, working from existing catalogue images, delivered either as a storefront embed or over Instagram DM, with multi-garment outfit try-on.
Being explicit about the limits, because a vendor who claims none is not describing this technology honestly:
- It shows appearance, not certified measurements. It is not a replacement for a size chart on made-to-measure tailoring.
- Output quality tracks input quality. A dark, cropped or heavily filtered shopper photo produces a weaker result.
- Rigid accessories with hard reflections — eyewear in particular — are a category where a well-built AR filter can still be the better tool.
Frequently asked questions
Which virtual try-on approach is best?
It depends on the category. AR overlay filters are best for rigid items with a stable anchor such as glasses and makeup. 3D garment simulation is best where physically accurate fit measurement is the goal. Generative AI on the shopper photo is best for apparel at catalogue scale, because it needs no per-garment 3D work and shows the actual shopper rather than an avatar.
Is generative virtual try-on accurate about size?
It is accurate about appearance, not about certified measurement. It shows how a garment looks and falls on a person; it does not replace a size chart where exact measurements are required.
Why not just use an AR filter for clothing?
AR filters draw a graphic over a camera feed and cannot represent how fabric drapes, folds or hangs with weight. That is acceptable for glasses or a watch and unconvincing for a dress or a jacket.
Does virtual try-on need a 3D model of each garment?
Not with the generative approach — an existing catalogue photo is enough. This is the main reason generative systems reach far higher catalogue coverage than 3D simulation, and coverage is what determines revenue impact.
What is the most common quality failure to check for?
Identity drift: the face subtly changing, skin being smoothed, or hair losing colour. Always review outputs at full resolution rather than as thumbnails, because thumbnails hide exactly this class of defect.